Automated Analysis of Microscopic Images of Isolated Pancreatic Islets

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F16%3A43901595" target="_blank" >RIV/60461373:22340/16:43901595 - isvavai.cz</a>

Alternative codes found

RIV/67985556:_____/16:00465945 RIV/68407700:21230/16:00304584 RIV/00023001:_____/16:00060180

Result on the web

<a href="http://docserver.ingentaconnect.com.ezproxy.vscht.cz/deliver/connect/cog/09636897/v25n12/s5.pdf?expires=1486165890&id=89827820&titleid=5476&accname=Institute+of+Chemical+Technology%2C+Prague&checksum=42223F3A4B4E1B81746F54F2DC1FF32A" target="_blank" >http://docserver.ingentaconnect.com.ezproxy.vscht.cz/deliver/connect/cog/09636897/v25n12/s5.pdf?expires=1486165890&id=89827820&titleid=5476&accname=Institute+of+Chemical+Technology%2C+Prague&checksum=42223F3A4B4E1B81746F54F2DC1FF32A</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3727/096368916X692005" target="_blank" >10.3727/096368916X692005</a>

Result language

angličtina

Original language name

Automated Analysis of Microscopic Images of Isolated Pancreatic Islets

Original language description

Clinical islet transplantation programs rely on the capacities of individual centers to quantify isolated islets. Current computer-assisted methods require input from human operators. Here, we describe two machine learning algorithms for islet quantification, the trainable islet algorithm (TIA) and the non-trainable purity algorithm (NPA). These algorithms automatically segment pancreatic islets and exocrine tissue on microscopic images in order to count individual islets and calculate islet volume and purity. References for islet counts and volumes were generated by the fully manual segmentation (FMS) method, which was validated against the internal DNA standard. References for islet purity were generated via the expert visual assessment (EVA) method, which was validated against the FMS method. The TIA is intended to automatically evaluate micrographs of isolated islets from future donors, after being trained on micrographs from a limited number of past donors. Its training ability was first evaluated on 46 images from four donors. The pixel-to-pixel comparison, binary statistics, and islet DNA concentration indicated that the TIA was successfully trained, regardless of the color differences of the original images. Next, the TIA trained on the four donors was validated on an additional 36 images from nine independent donors. The TIA was fast (67 sec/image), correlated very well with the FMS method (R2 = 1.00 and 0.92 for islet volume and islet count, respectively), and had small REs (0.06 and 0.07 for islet volume and islet count, respectively). Validation of the NPA against the EVA method using 70 images from 12 donors revealed that the NPA had a reasonable speed (69 sec/image), an acceptable RE (0.14), and correlated well with the EVA method (R2 = 0.88). Our results demonstrate that a fully automated analysis of clinical-grade micrographs of isolated pancreatic islets is feasible. The algorithms described herein will be freely available as a Fiji platform plugin.

Czech name

—

Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

—

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Cell Transplantation

ISSN

0963-6897

e-ISSN

—

Volume of the periodical

25

Issue of the periodical within the volume

12

Country of publishing house

US - UNITED STATES

Number of pages

12

Pages from-to

2145-2156

UT code for WoS article

000390183200005

EID of the result in the Scopus database

2-s2.0-85007086435